STAT2 by Ann R. Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore; Allan J. Rossman; Jeffrey A. Witmer - First Edition, 2014 from Macmillan Student Store

STAT2

First  Edition©2014  New Edition Available Ann R. Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore; Allan J. Rossman; Jeffrey A. Witmer

About

STAT2 offers students who have taken AP Statistics or a typical introductory statistics college level course to learn more sophisticated concepts and the tools with which to apply them.
 
The authors' primary goal is to help students gain facility in the use of common statistical models.  The text instructs students on working with models where the response variable is either quantitative or categorical and predictors (or explanatory factors) are quantitative or categorical (or both).  The chapters are grouped to consider models based on the type of response and type of predictors.
 
After completing a course with STAT2 students should be able to:
1. Choose the appropriate statistical model for a particular problem.
2. Know the conditions that are typically required when fitting various models.
3. Assess whether or not the conditions for a particular model are reasonably met for a specific dataset. 4. Have some strategies for dealing with data when the conditions for a standard model are not met.
5. Use the appropriate model to make appropriate inferences.

Table of Contents

0 What Is a Statistical Model?
0.1 Fundamental Terminology
0.2 Four-Step Process
0.3 Chapter Summary
0.4 Exercises

Unit A: Linear Regression

1 Simple Linear Regression
1.1 The Simple Linear Regression Model
1.2 Conditions for a Simple Linear Model
1.3 Assessing Conditions
1.4 Transformations
1.5 Outliers & Influential Points
1.6 Chapter Summary
1.7 Exercises

2 Inference for Simple Linear Regression
2.1 Inference for Regression Slope
2.2 Partitioning Variability - ANOVA
2.3 Regression and Correlation
2.4 Intervals for Predictions
2.5 Chapter Summary
2.6 Exercises

3 Multiple Regression
3.1 Multiple Linear Regression Model
3.2 Assessing a Multiple Regression Model
3.3 Comparing Two Regression Lines
3.4 New Predictors from Old
3.5 Correlated Predictors
3.6 Testing Subsets of Predictors
3.7 Case Study: Predicting in Retail Clothing
3.8 Chapter Summary
3.9 Exercises

4 Additional Topics in Regression
4.1 Topic: Added Variable Plots
4.2 Topic: Techniques for Choosing Predictors
4.3 Topic: Identifying Unusual Points in Regression
4.4 Topic: Coding Categorical Predictors
4.5 Topic: Randomization Test for a Relationship
4.6 Topic: Bootstrap for Regression
4.7 Exercises

Unit B: Analysis of Variance

5 One-way ANOVA
5.1 The One-way Model: Comparing Groups
5.2 Assessing and Using the Model
5.3 Scope of Inference
5.4 Fisher’s Least Significant Difference
5.5 Chapter Summary
5.6 Exercises

6 Multifactor ANOVA
6.1 The Two-way Additive Model (Main Effects Model)
6.2 Interaction in the Two-way Model
6.3 The Two-way Non-additive Model (Two-Way ANOVA with Interaction)
6.4 Case Study
6.5 Chapter Summary
6.6 Exercises

7 Additional Topics in Analysis of Variance
7.1 Topic: Levene’s Test for Homogeneity of Variances
7.2 Topic: Multiple Tests
7.3 Topic: Comparisons and Contrasts
7.4 Topic: Nonparametric Statistics
7.5 Topic: ANOVA and Regression with Indicators
7.6 Topic: Analysis of Covariance
7.7 Exercises

8 Overview of Experimental Design
8.1 Comparisons and Randomization
8.2 Randomization F Test
8.3 Design Strategy: Blocking
8.4 Design Strategy: Factorial Crossing
8.5 Chapter Summary
8.6 Exercises

Unit C: Logistic Regression

9 Logistic Regression
9.1 Choosing a Logistic Regression Model
9.2 Logistic regression and odds ratios
9.3 Assessing the logistic regression model
9.4 Formal inference: tests and intervals
9.5 Summary
9.6 Exercises

10 Multiple Logistic Regression
10.1 Overview
10.2 Choosing, fitting, and interpreting models
10.3 Checking conditions
10.4 Formal inference: tests and intervals
10.5 Case Study: Bird Nests
10.6 Summary
10.7 Exercises

11 Additional Topics in Logistic Regression
11.1 Topic: Fitting the logistic regression model
11.2 Topic: Assessing Logistic Regression Models
11.3 Randomization Tests
11.4 Analyzing Two-way Tables with Logistic Regression
11.5 Exercises

Ann R. Cannon

Ann R. Cannon has been a faculty member at Cornell College since 1993. She is currently Watson M. Davis Professor of Mathematics and Statistics in the Department of Mathematics and Statistics. She is the 2017 recipient of the Mu Sigma Rho William D. Warde Statistics Education Award. She has served terms as secretary/treasurer and at-large member of the executive committee for the Stat-Ed section as well as Council of Sections rep for Stat-Ed and as Treasurer (8 years) and President (1 year) for the Iowa Chapter of the ASA. She was Associate editor for JSE from 2000 to 2009 and was moderator for Isostat from 2003 to 2007. She has been reader, table leader, question leader, and assistant chief reader for the AP Statistics exam. She is also currently serving on the School Board for the Lisbon Community School District.

George W. Cobb

George Cobb is Robert l. Rooke Professor emeritus at Mount Holyoke College, where he taught from 1974 to 2009 after earning his PhD in statistics from Harvard University.  He is a Fellow of the American Statistical Association, served a term as ASA vice-president, and received the ASA Founder’s award.  He is also recipient of the of the Lifetime Achievement award of the US Conference on Teaching Statistics.  He is author or co-author of several books, including Introduction to Design and Analysis of Experiments and Statistics in Action.  His interests include Markov chain Monte Carlo, applications of statistics to the law, and bluegrass banjo.

Bradley A. Hartlaub

Brad Hartlaub is a Professor in the Department of Mathematics and Statistics at Kenyon College. He is a nonparametric statistician who has served as the Chief Reader of the AP Statistics Program and is an active member of the American Statistical Association's Section on Statistical Education. Brad was selected as a Fellow of the American Statistical Association in 2006. He has served the College as a department chair, a division chair, a supervisor of undergraduate research, and an associate provost. He has received research grants to support his work with undergraduate students from the Andrew W. Mellon Foundation, the Council on Undergraduate Research, and the National Science Foundation. Brad received the Trustee Award for Distinguished Teaching in 1996, and the Distinction in Mentoring Award in 2014.

Julie M. Legler

Julie Legler earned a BA and MS in Statistics from the University of Minnesota and later a doctorate in biostatistics from Harvard.  She has taught statistics at the undergraduate level for nearly 20 years. In addition, she spent 7 years at the National Institutes of Health,  first as a postdoc and then as a mathematical statistician at the National Cancer Institute.  She has published in the areas of latent variable modeling, surveillance modeling, and undergraduate research.  Currently she is professor of statistics and director of the Statistics Program at St. Olaf College.  Recently she was named the Director of Collaborative Undergraduate Research and Inquiry  at St. Olaf.

Robin H. Lock

Robin H. Lock is the Jack and Sylvia Burry Professor of Statistics at St. Lawrence University where he has taught since 1983 after receiving his PhD from the University of Massachusetts- Amherst. He is a Fellow of the American Statistical Association, past Chair of the Joint MAA-ASA Committee on Teaching Statistics, a member of the committee that developed GAISE (Guidelines for Assessment and Instruction in Statistics Education), and on the editorial board of CAUSE (the Consortium for the Advancement of Undergraduate Statistics Education). He has won the national Mu Sigma Rho Statistics Education award and numerous awards for presentations on statistics education at national conferences.

Thomas L. Moore

Thomas Moore earned a B.A. from Carleton College, an M.S. from the University of Iowa, and a Ph.D. from Dartmouth.  He has been on the faculty at Grinnell College since 1980 and has concentrated his scholarship on statistics education.  He chaired the Statistics Education Section of ASA in 1995 and the MAA's SIGMAA for Statistics Education in 2004.  He is a Fellow of American Statistical Association and was the2008 Mu Sigma Rho Statistical Education Award winner.

Allan J. Rossman

Allan J. Rossman is Professor and Chair of the Statistics Department at Cal Poly – San Luis Obispo. He served as Chief Reader of the Advanced Placement program in Statistics from 2009-2014. He was Program Chair for the 2007 Joint Statistical Meetings and for the U.S. Conference in Teaching Statistics since 2013. He is a Fellow of the American Statistical Association and has received the Mathematical Association of America’s Haimo Award for Distinguished College or University Teaching of Mathematics and the ASA’s Waller Distinguished Teaching Career Award.

Jeffrey A. Witmer

Jeff Witmer is Professor of Mathematics at Oberlin College.  He earned a doctorate in statistics from the University of Minnesota in 1983. His scholarly work has been primarily in the areas of Bayesian decision theory and statistics education.  He is a Fellow of the American Statistical Association and served as editor of STATS magazine.  Among the books he has written or co-authored are Activity Based Statistics and Statistics for the Life Sciences.